Abstract
Seismocardiogram (SCG) recording is a novel method for the prolonged monitoring of the cardiac mechanical performance during spontaneous behavior. The continuous monitoring results in a collection of thousands of beats recorded during a variety of physical activities so that the automatic analysis and processing of such data is a challenging task due to the presence of artefactual beats and morphological changes over time that currently request the human expertise. On this premise, we propose the use of the Evolving Fuzzy Neural Network (EFuNN) paradigm for the automatic artifact detection in the SCG signal. The fuzzy logic processing method can be applied to model the human expertise knowledge using the learning capabilities of an artificial neural network. The evolving capability of the EFuNN paradigm has been applied to solve the issue of the physiological variability of the SGC waveform. Preliminary tests have been carried out to validate this approach and the obtained results demonstrate the effectiveness of the method and its scalability.
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Acknowledgements
The SCG data collection and the work of MDR, EV and PL were supported by the Italian Space Agency through the ASI 2013–061-I.0 and ASI 2013–079-R.0 grants.
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Malcangi, M., Quan, H., Vaini, E., Lombardi, P., Di Rienzo, M. (2017). Applying the EFuNN Evolving Paradigm to the Recognition of Artefactual Beats in Continuous Seismocardiogram Recordings. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_22
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DOI: https://doi.org/10.1007/978-3-319-65172-9_22
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